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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q95-Q100):

NEW QUESTION # 95
A data analyst is tasked with optimizing a daily ETL pipeline that loads data from several external sources into a Snowflake data warehouse. One of the key transformations involves joining two large tables, 'ORDERS' (millions of rows) and 'CUSTOMERS' (hundreds of thousands of rows), based on 'CUSTOMER ID. The pipeline currently uses a standard 'JOIN' operation, but the transformation step is taking longer than expected. The analyst has explored various optimization techniques, including increasing virtual warehouse size, but the performance improvement is minimal. Assuming that the 'CUSTOMER ID column is appropriately indexed (or clustered, if applicable) in both tables, and you want to minimise data movement. Which of the following approaches would yield the MOST significant performance improvement for this transformation step, considering metadata caching and data distribution?

A. Pre-sort both tables by 'CUSTOMER_ID before the join operation using 'ORDER BY clauses in subqueries, ensuring that the data is co-located for faster processing.
B. Use a CTAS (CREATE TABLE AS SELECT) statement with the = HASH' clause on the 'CUSTOMER_ID column to redistribute the 'CUSTOMERS table before the join operation.
C. Use a 'BROADCAST JOIN' hint to force Snowflake to distribute the smaller 'CUSTOMERS' table to all nodes in the virtual warehouse, regardless of the table sizes and statistics.
D. Ensure that the 'CUSTOMER_ID column in both 'ORDERS' and 'CUSTOMERS' tables have the same data type and collation. Then, leverage Snowflake's automatic optimization capabilities without explicit hints or redistribution.
E. Implement a 'MAP JOIN' by setting = FALSE at session Level, which caches the smaller table ('CUSTOMERS') in memory on each node of the virtual warehouse.

Answer: D

Explanation:
Ensuring consistent data types and collations (D) is crucial for optimal join performance in Snowflake. When data types and collations match, Snowflake can leverage its internal optimizations more effectively, including metadata caching and efficient data access patterns. Using a 'BROADCAST JOIN' hint without considering data distribution (A) might lead to unnecessary data movement and performance degradation. 'MAP JOIN' is unavailable in Snowflake (B). Redistributing data using 'DISTRIBUTION_TYPE = HASH' (C) is generally less efficient than leveraging Snowflake's automatic optimizations. Pre-sorting data (E) is unnecessary in Snowflake as it does not guarantee data colocation in the same way that other distributed systems might.

NEW QUESTION # 96
You are tasked with creating a dashboard to monitor the performance of different marketing channels (e.g., email, social media, paid advertising). The data includes daily spend, impressions, clicks, and conversions for each channel. Which approach would BEST allow you to visualize the return on investment (ROI) for each channel over time, identify channels with diminishing returns, and enable stakeholders to easily compare channel performance?

A. Create separate line charts for each channel showing spend, impressions, clicks, and conversions over time, using a static reporting tool like SSRS.
B. Export the data to Excel and create a pivot table summarizing spend and conversions for each channel. Generate a simple bar chart showing total ROI for each channel.
C. Use Snowflake's built-in charting capabilities to create a series of pie charts showing the percentage of total spend allocated to each channel.
D. Create a static report in Tableau using only aggregate measures to calculate the total ROI for each channel and display it in a table.
E. Develop an interactive dashboard in Looker Studio, utilizing calculated fields to derive ROI for each channel (e.g., conversions / spend). Use a combination of line charts, bar charts (ROI per channel), and scatter plots (spend vs. conversions) with trendlines. Implement drill-down capabilities to view daily performance metrics.

Answer: E

Explanation:
Option B is the most suitable because it uses an interactive dashboard (Looker Studio) with calculated fields to derive ROI. The combination of line charts, bar charts, and scatter plots provides a comprehensive view of channel performance over time. Trendlines in the scatter plots help identify diminishing returns. Drill-down capabilities allow for detailed analysis. Option A creates separate charts, making comparison difficult. Option C is limited to summary data. Option D focuses on spend allocation, not ROI. Option E provides only a static view of total ROI.

NEW QUESTION # 97
You are preparing to load data from an internal stage that contains multiple CSV files into a Snowflake table. You need to ensure that only files matching a specific pattern are loaded (e.g., files starting with 'sales_'). You also need to transform the date column (located in the third position of each CSV file) from 'YYYYMMDD' to 'YYYY-MM-DD' format during the load. The target table 'SALES has columns 'product_id', and 'region'. Which combination of options correctly filters the files and transforms the date format during the load process?

A. Option A
B. Option B
C. Option C
D. Option E
E. Option D

Answer: E

Explanation:
Option D is correct. It uses the 'PATTERN' parameter to filter files starting with 'sales_' and ending with '.csv'. It then uses the TRANSFORM AS SELECT clause to select and transform the data. Inside the select clause, it correctly uses 'substr' to reformat the date and 'TO_DATE to convert the re-formatted string into a DATE datatype. The 'ON_ERROR = 'SKIP_FILE" is a good practice for handling potential errors. Option A has an invalid parameter declaration within the COPY INTO command and does not use PATTERN, making the filter inaccurate. Option B attempts to define date format at file format level, but date transformatinon is required and FILES filter declaration is invalid. Option C uses 'TRANSFORMATION' instead of 'TRANSFORM AS SELECT , which is incorrect and will cause an error. Option E attempts to define DATE FORMAT with the wrong TYPE and is missing concatination of DATE string.

NEW QUESTION # 98
You have a Snowflake table 'EMPLOYEES' with columns 'EMPLOYEE ID' ONT, PRIMARY KEY), 'SALARY' and 'DEPARTMENT' (VARCHAR). You need to enforce the following business rules: 1. 'SALARY' must be a positive value. 2. 'DEPARTMENT' must be one of the following values: 'SALES', 'MARKETING', 'ENGINEERING'. 3. If the Employee is in 'SALES' Department, Salary should be between 50000 and 100000. Which of the following is the most appropriate and efficient approach using Snowflake constraints and features?

A. Use CHECK constraint for SALARY, Create a Lookup table for Departments, and apply a Foreign key relationship for DEPARTMENT field in EMPLOYEE table.
B. Use CHECK constraints for both rules and a third CHECK constraint to combine rules 2 and 3 to apply on each record.
C. Enforce rules using stored procedure at the time of insertion and updation.
D. Use a CHECK constraint for 'SALARY > , an ENUM type for 'DEPARTMENT' , and a TRIGGER to enforce the salary range rule for the 'SALES' department.
E. Use a CHECK constraint for 'SALARY > , a CHECK constraint 'DEPARTMENT IN ('SALES', 'MARKETING', 'ENGINEERING') , and a third CHECK constraint 'CASE WHEN DEPARTMENT = 'SALES' THEN SALARY BETWEEN 50000 AND 100000 ELSE TRUE END'.

Answer: E

Explanation:
Option D is the most appropriate and efficient. CHECK constraints are designed for these types of validations. The 'CASE' statement within the third CHECK constraint allows conditional validation based on the 'DEPARTMENT value. CHECK constriants are enforced at the time of record insert or update. Stored procedures could be an option but are not the most appropriate. Snowflake does not directly support ENUM types for column definitions. Creating Lookup table with Foreign key is another option.

NEW QUESTION # 99
You are analyzing sales data from different regions stored in a Snowflake table named 'sales_data'. The table includes columns: 'transaction_id' (VARCHAR), 'region' (VARCHAR), 'sale_date' (DATE), and 'sale_amount' (NUMBER). You discover the following data quality issues: The 'region' column contains inconsistent entries such as 'North', 'north', 'NOrth ', and ' South'. The 'sale_amount' column has some values that are stored as strings (e.g., '100.50') instead of numbers, causing errors in aggregation. There are duplicate records identified by the same 'transaction id'. Which set of SQL statements, executed in the given order, provides the MOST effective and efficient way to address these data quality issues in Snowflake?

Answer: D

Explanation:
Option E presents the most efficient and effective solution. It combines all three data cleaning steps into a single operation using a CTE. First, standardizes the region name with trim and lowercase. Second, remove duplicate records based on transaction ID. And most important, it correctly handles the 'sale_amount' conversion using TRY_TO_NUMBER inside the CTE to avoid errors and ensures accurate aggregations down stream. This approach minimizes the number of table scans and UPDATE operations, improving performance. Option A fails on how to remove duplicates correctly using TRY_TO_NUMBER to convert the sale amount correctly and data type changes are not possible via ALTER statements if strings are present. Options B, C, and D does not combine all in one single CTE operations and are slower.

NEW QUESTION # 100
......

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PASS-SURE DAA-C01 PDF DOWNLOAD OFFER YOU THE BEST DEMO TEST | SNOWFLAKE SNOWPRO ADVANCED: DATA ANALYST CERTIFICATION EXAM

Pass-Sure DAA-C01 PDF Download Offer You The Best Demo Test | Snowflake SnowPro Advanced: Data Analyst Certification Exam